Evaluating Artificial Intelligence-Based Clinical Decision Support for Sepsis and ARDS

February 16, 2026 updated by: University of Pennsylvania

Evaluating Artificial Intelligence-Based Comprehensive Clinical Decision Support for Sepsis and ARDS

Sepsis and acute respiratory distress syndrome (ARDS) are common in intensive care units. Managing sepsis and ARDS is inherently complex and requires making numerous decisions under uncertainty. Artificial intelligence (AI) clinical decision support systems (CDSSs) offer a promising approach to support care management for sepsis and ARDS.

The goal of this randomized, survey-based study is to compare treatment recommendations enacted by clinicians to those generated by an AI CDSS. The study will investigate whether an AI CDSS can generate treatment recommendations that are safe, appropriate, and indistinguishable to those provided by real clinicians.

In this study, participants (i.e., critical care clinicians) will review a series of critical care cases (vignettes) in an electronic survey. Each vignette will contain a de-identified case of a patient with sepsis and ARDS as well as treatment recommendations for the case. Participants will assess the safety and appropriateness of each treatment recommendations and answer whether they think the treatment recommendations came from the clinician or an AI CDSS.

Study Overview

Status

Enrolling by invitation

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

350

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • Pennsylvania
      • Philadelphia, Pennsylvania, United States, 19104
        • University of Pennsylvania

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Working as a physician (i.e., MD, DO) or an advanced practice provider (i.e., nurse practitioner, physician assistant)
  • Working at a hospital or medical center in medical critical care, anesthesia critical care, surgical critical care, or emergency medicine

Exclusion Criteria:

  • Has not completed a residency training program (i.e., medical intern or resident)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Artificial Intelligence
Critical care cases / vignettes in this arm will contain treatment recommendations generated by an artificial intelligence-based clinical decision support system. Each participant will review four vignettes from this arm.
Other: Artifical Intelligence-Generated Treatment Recommendations
The clinical vignette will contain treatment recommendations which were generated by an artificial intelligence-based clinical decision support system.
No Intervention: Human Clinician
Critical care cases / vignettes in this arm will contain treatment recommendations that were enacted by the clinician in the actual case. Each participant will review four vignettes from this arm.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of Predicting the Source of Treatment Recommendation
Time Frame: From enrollment to the end of the survey, an average of 45 minutes
Participants will answer if they think the treatment recommendations came from artificial intelligence (AI) or a clinician for each clinical vignette. Accuracy will be measured by participants correctly identifying the source of treatment recommendation.
From enrollment to the end of the survey, an average of 45 minutes

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Confidence of Predicting the Source of Treatment Recommendation
Time Frame: From enrollment to the end of the survey, an average of 45 minutes
Participants will respond to their confidence in their prediction in whether the treatment recommendations of a vignette came from artificial intelligence or from a clinician. Confidence will measured on a Likert scale ranging from 0 (Not at all confident) to 7 (Extremely confident).
From enrollment to the end of the survey, an average of 45 minutes
Appropriateness of Treatment Recommendations
Time Frame: From enrollment to the end of the survey, an average of 45 minutes
Appropriateness will be measured by participants' assessments of the clinical appropriateness of the treatment recommendations in the vignettes via Yes-No and free-text responses.
From enrollment to the end of the survey, an average of 45 minutes
Safety of Treatment Recommendations
Time Frame: From enrollment to the end of the survey, an average of 45 minutes
Safety will be measured by participants' assessments of the overall safety of the treatment recommendations in the vignettes via Yes-No and free-text responses.
From enrollment to the end of the survey, an average of 45 minutes

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Pennsylvania

Collaborators

National Institute of General Medical Sciences (NIGMS)

Investigators

  • Principal Investigator: Gary Weissman, MD, MSHP, University of Pennsylvania

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 5, 2025

Primary Completion (Estimated)

May 1, 2026

Study Completion (Estimated)

May 1, 2026

Study Registration Dates

First Submitted

May 30, 2025

First Submitted That Met QC Criteria

June 9, 2025

First Posted (Actual)

June 17, 2025

Study Record Updates

Last Update Posted (Actual)

February 17, 2026

Last Update Submitted That Met QC Criteria

February 16, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 858201
  • R35GM155262 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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